| Daily water level forecasting using wavelet decomposition and artificial intelligence techniques Y Seo, S Kim, O Kisi, VP Singh Journal of Hydrology 520, 224-243, 2015 | 306 | 2015 |
| Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling S Kim, HS Kim Journal of Hydrology 351 (3-4), 299-317, 2008 | 294 | 2008 |
| Groundwater level prediction using machine learning models: A comprehensive review Neurocomputing 489, 271-308, 2022 | 165 | 2022 |
| Shear strength prediction of steel fiber reinforced concrete beam using hybrid intelligence models: a new approach ZM Yaseen, MT Tran, S Kim, T Bakhshpoori, RC Deo Engineering Structures 177, 244-255, 2018 | 127 | 2018 |
| A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions M Alizamir, S Kim, O Kisi, M Zounemat-Kermani Energy 197, 117239, 2020 | 117 | 2020 |
| Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model A Malik, A Kumar, S Kim, MH Kashani, V Karimi, A Sharafati, MA Ghorbani, ... Engineering Applications of Computational Fluid Mechanics 14 (1), 323-338, 2020 | 104 | 2020 |
| Drought forecasting using novel heuristic methods in a semi-arid environment O Kisi, AD Gorgij, M Zounemat-Kermani, A Mahdavi-Meymand, S Kim Journal of Hydrology 578, 124053, 2019 | 102 | 2019 |
| Pan evaporation modeling using neural computing approach for different climatic zones S Kim, J Shiri, O Kisi Water Resources Management 26, 3231-3249, 2012 | 97 | 2012 |
| Novel hybrid data-intelligence model for forecasting monthly rainfall with uncertainty analysis ZM Yaseen, I Ebtehaj, S Kim, H Sanikhani, H Asadi, MI Ghareb, ... Water 11 (3), 502, 2019 | 94 | 2019 |
| Modeling daily soil temperature using data-driven models and spatial distribution S Kim, VP Singh Theoretical and Applied Climatology 118, 465-479, 2014 | 94 | 2014 |
| Estimating daily pan evaporation using different data-driven methods and lag-time patterns S Kim, J Shiri, O Kisi, VP Singh Water Resources Management 27, 2267-2286, 2013 | 91 | 2013 |
| Estimating daily reference evapotranspiration using available and estimated climatic data by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) A Pour-Ali Baba, J Shiri, O Kisi, AF Fard, S Kim, R Amini Hydrology Research 44 (1), 131-146, 2013 | 85 | 2013 |
| Advanced machine learning model for better prediction accuracy of soil temperature at different depths M Alizamir, O Kisi, AN Ahmed, C Mert, CM Fai, S Kim, NW Kim, ... PLoS ONE 15 (4), e0231055, 2020 | 84 | 2020 |
| River salinity prediction using hybrid machine learning models AM Melesse, K Khosravi, JP Tiefenbacher, S Heddam, S Kim, A Mosavi, ... Water 12 (10), 2951, 2020 | 83 | 2020 |
| Daily river flow forecasting using ensemble empirical mode decomposition based heuristic regression models: Application on the perennial rivers in Iran and South Korea M Rezaie-Balf, S Kim, H Fallah, S Alaghmand Journal of Hydrology 572, 470-485, 2019 | 71 | 2019 |
| Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India A Malik, A Kumar, SQ Salih, S Kim, NW Kim, ZM Yaseen, VP Singh PLoS ONE 15 (5), e0233280, 2020 | 67 | 2020 |
| Estimating spatial precipitation using regression kriging and artificial neural network residual kriging (RKNNRK) hybrid approach Y Seo, S Kim, VP Singh Water Resources Management 29, 2189-2204, 2015 | 66 | 2015 |
| Can decomposition approaches always enhance soft computing models? Predicting the dissolved oxygen concentration in the St. Johns River, Florida M Zounemat-Kermani, Y Seo, S Kim, MA Ghorbani, S Samadianfard, ... Applied Sciences 9 (12), 2534, 2019 | 62 | 2019 |
| Machine learning models coupled with variational mode decomposition: a new approach for modeling daily rainfall-runoff Y Seo, S Kim, VP Singh Atmosphere 9 (7), 251, 2018 | 61 | 2018 |
| Predicting daily pan evaporation by soft computing models with limited climatic data S Kim, J Shiri, VP Singh, O Kisi, G Landeras Hydrological Sciences Journal 60 (6), 1120-1136, 2015 | 60 | 2015 |
Ozgur KisiDepartment of Civil Engineering, University of Applied Sciences, Lübeck, GermanyVerified email at th-luebeck.de
